Publications

In this article we describe a new method, using SPM99, that searches explicitly for bilateral structural abnormalities. Children with bilateral pathology have a poorer prognosis than children with unilateral damage. After brain injury or disease in childhood, it is thought that rescue of function is only possible if the neuronal substrates of that function are preserved and operational in at least one hemisphere [Vargha-Khadem and Mishkin, 1997]. If this is the case, the detection of bilateral abnormalities would greatly facilitate more accurate prognosis in children with brain injury or developmental disorders. We have therefore developed a technique to detect bilateral abnormalities that uses conjunction analysis with voxel based morphometry. It is illustrated using a group of patients with bilateral hypoxic-ischaemic damage to the hippocampus. The approach is shown to have enhanced specificity and sensitivity relative to conventional unilateral characterisations.

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space - identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory-inhibitory balance.

Previous functional neuroimaging studies have characterized brain systems mediating associative learning using classical delay conditioning paradigms. In the present study, we used event-related functional magnetic resonance imaging to characterize neuronal responses mediating aversive trace conditioning. During conditioning, neutral auditory tones were paired with an aversive sound [unconditioned stimulus (US)]. We compared neuronal responses evoked by conditioned (CS+) and nonconditioned (CS−) stimuli in which a 50% pairing of CS+ and the US enabled us to limit our analysis to responses evoked by the CS+ alone. Differential responses (CS+ vs CS−), related to conditioning, were observed in anterior cingulate and anterior insula, regions previously implicated in delay fear conditioning. Differential responses were also observed in the amygdala and hippocampus that were best characterized with a time × stimulus interaction, indicating rapid adaptation of CS+-specific responses in medial temporal lobe. These results are strikingly similar to those obtained with a previous delay conditioning experiment and are in accord with a preferential role for medial temporal lobe structures during the early phase of conditioning. However, an additional activation of anterior hippocampus in the present experiment supports a view that its role in trace conditioning is to maintain a memory trace between the offset of the CS+ and the delayed onset of the US to enable associative learning in trace conditioning.

This paper considers psychotic symptoms in terms of false inferences or beliefs. It is based on the notion that the brain is an inference machine that actively constructs hypotheses to explain or predict its sensations. This perspective provides a normative (Bayes-optimal) account of action and perception that emphasizes probabilistic representations; in particular, the confidence or precision of beliefs about the world. We will consider hallucinosis, abnormal eye movements, sensory attenuation deficits, catatonia, and delusions as various expressions of the same core pathology: namely, an aberrant encoding of precision. From a cognitive perspective, this represents a pernicious failure of metacognition (beliefs about beliefs) that can confound perceptual inference. In the embodied setting of active (Bayesian) inference, it can lead to behaviors that are paradoxically more accurate than Bayes-optimal behavior. Crucially, this normative account is accompanied by a neuronally plausible process theory based upon hierarchical predictive coding. In predictive coding, precision is thought to be encoded by the post-synaptic gain of neurons reporting prediction error. This suggests that both pervasive trait abnormalities and florid failures of inference in the psychotic state can be linked to factors controlling post-synaptic gain - such as NMDA receptor function and (dopaminergic) neuromodulation. We illustrate these points using biologically plausible simulations of perceptual synthesis, smooth pursuit eye movements and attribution of agency - that all use the same predictive coding scheme and pathology: namely, a reduction in the precision of prior beliefs, relative to sensory evidence.

Abstract In this paper, we introduce a word repetition generative model (WORM), which—when combined with an appropriate belief updating scheme—is capable of inferring the word that should be spoken when presented with an auditory cue. Our generative model takes a deep temporal form, combining both discrete and continuous states. This allows a (synthetic) WORM agent to perform categorical inference on continuous acoustic signals, and—based on the same model—to repeat heard words at the appropriate time. From the perspective of word production, the model simulates how high-level beliefs about discrete lexical, prosodic and context attributes give rise to continuous acoustic signals at the sensory level. From the perspective of word recognition, it simulates how continuous acoustic signals are recognised as words and, how (and when) they should be repeated. We establish the face validity of our generative model by simulating a word repetition paradigm in which a synthetic agent or a human subject hears a target word and subsequently reproduces that word. The repeated word should be the target word but differs acoustically. The results of these simulations reveal how the generative model correctly infers what must be repeated, to the extent it can successfully interact with a human subject. This provides a formal process theory of auditory perception and production that can be deployed in health and disease. We conclude with a discussion of how the generative model could be scaled-up to include a larger phonetic and phonotactic repertoire, complex higher-level attributes (e.g., semantic, concepts, etc.), and produce more elaborate exchanges.

In previous papers, we introduced a normative scheme for scene construction and epistemic (visual) searches based upon active inference. This scheme provides a principled account of how people decide where to look, when categorising a visual scene based on its contents. In this paper, we use active inference to explain the visual searches of normal human subjects; enabling us to answer some key questions about visual foraging and salience attribution. First, we asked whether there is any evidence for ‘epistemic foraging’; i.e. exploration that resolves uncertainty about a scene. In brief, we used Bayesian model comparison to compare Markov decision process (MDP) models of scan-paths that did–and did not–contain the epistemic, uncertainty-resolving imperatives for action selection. In the course of this model comparison, we discovered that it was necessary to include non-epistemic (heuristic) policies to explain observed behaviour (e.g., a reading-like strategy that involved scanning from left to right). Despite this use of heuristic policies, model comparison showed that there is substantial evidence for epistemic foraging in the visual exploration of even simple scenes. Second, we compared MDP models that did–and did not–allow for changes in prior expectations over successive blocks of the visual search paradigm. We found that implicit prior beliefs about the speed and accuracy of visual searches changed systematically with experience. Finally, we characterised intersubject variability in terms of subject-specific prior beliefs. Specifically, we used canonical correlation analysis to see if there were any mixtures of prior expectations that could predict between-subject differences in performance; thereby establishing a quantitative link between different behavioural phenotypes and Bayesian belief updating. We demonstrated that better scene categorisation performance is consistently associated with lower reliance on heuristics; i.e., a greater use of a generative model of the scene to direct its exploration.

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.

No abstract is provided for this article.